> MCP makes all of that so much easier to provide.
Easier being the key!
Right now it really does feel like early web days where like 5% of the population is adopting things, but most people just get confused and don't participate.
This is why I'm pretty excited about WebMCP in particular!
WebMCP aligns more stakeholders than pure MCP - which seems heavily biased towards model providers. It also seems like it has the potential for a much cleaner ux.
In some sense, I can't think of a worse group for this to happen to, but a better group for who might come up with a solution and help humanity forge a new path.
I've long been of the opinion that mathematicians are probably the smartest and most clever workforce around.
They really might be our best hope to solving this.
I've often secretly wished that some of these clever folks would get their head out of the books and solve some real world problems. This might just be a forcing function for that.
I can't imagine the social disruption of for example it had been trucking or construction or some other industry which might react more combatively and less constructively to complete automation.
I'm no crypto expert at all but isn't TRUPPENVERSCHIEBUNG an actual word?
Google says it translates to "troops shift"
If so, I don't understand how this was difficult at all... Can't you just do a dictionary attack and then check if the resulting phrase forms a sentence?
I don't understand how this couldn't be done by someone in their house with access to a computer and a German dictionary.
Maybe I'm messing something? Was the rearranging of the letters random in some non-deterministic way that wasn't known up front?
The article even says it was "easier" than that - it is a word in a list of known codewords (but slightly off the expected time).
The hard part is not that it's impossible for you to do at home, it's that nobody in 100 years has felt it was worth enough effort to try through enough words until the effort was as simple as prompting the LLM to do the work.
Not every value of AI has to be in the realm of superhuman intelligence, that's just what makes for the most discussion.
Yeah, you're right. Writing a program to solve ciphers with dictionary keys was an assignment in my intro to cryptography course - there's a couple of easy tricks you can do on the output to rapidly filter to the right solution.
Speaking as someone who often tries to reduce my use of reduce by replacing it with map and filter where possible, for me, falling back to reduce is analogous to falling back to a while loop or a for loop: I avoid it if I can.
The problem with reduce is that it can do so much, and therefore it is less clear when reading it quickly what it might be doing.
By formalizing, they mean within a proof assistant like Lean or Rocq, not simply in prose in a textbook. I can attest, 40 hours per page is by no means an overestimate for this sort of work.
The point was that a textbook (where the 40hr/page estimate comes from) is cumulative/linear -- what you need for page n was defined / established on the preceding pages. But in a proof such as this you can call on any other published result (and those can do the same) so the dependency graph is (potentially) much bushier. Thus later pages of the proof should take far more than 40 hours to manually formalize.
The scale factor comes from this number in the article, seemingly an intuited estimate:
> Say a research article takes 20 times more effort to formalize than page in an undergraduate textbook.
That would suggest formalizing a 10-page research article might take 200 weeks (assuming 40h/wk) of effort, or about four years. Not a mathematician, I have no idea if that's in the ballpark.
I got a bit lost in the comment thread for that article, but I don't understand why LLMs are not considered self referential... They are auto regressive as one of the commenters pointed out, and Scott just sort of waved his hand and said that if auto regression is sufficient then things like Conway's game of Life would also qualify as self-referential.
I don't see why Conway's game of life should not be considered self-referential though... I mean it's isn't it Turing complete? I don't see how any definition of self-referentiality should require throwing out systems which are minimally turing complete... If Turing complete is not enough, doesn't that imply that computable artificial intelligence is impossible in the first place?
Biological minds in biological organisms are self referential in the way that you have a neural network that forms a model of the world. That model then "discovers" that it is "it self a part of the world" so it tries to model that part of the world (model it self). In this way some type of self referential "awareness" (or whatever you want to call it) is formed. That self model that contains awareness is then used to guide organisms behaviour. Causal Transformer LLMs don't work in this way, they have theoretical knowledge that they exist but its selfhood is not in this described way built on the self modelling that biological brains do.
All this being an empirically unproven theory/hypothesis. But an extremely strong one (if you ask me).
Alan Watts says, "Mind finds itself in a strange position. It (ie universe) is in me (modelling wise). I am in it (physically)." But the model only has access to its modelling of both it and itself.
Neither will I, but it looks like a super easy rule to add to an expert system or the system prompt. Anyway, don't most LLM know that you shouldnotbreak pasta?
There are definitely pure MCP use cases.
For web apps though, WebMCP seems like an awesome enabler: better ux and better stakeholder alignment
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